where appropriate, data base values are string and link to another data base entry

Task:

given a person, org, or location:

if not in data base, create data base entry from news articles

if already in data base, fill in missing attributes in entry

Recall important

Need to extract from single instances

Need to extract when arguments are anaphoric

Relation Extraction (cont'd)

As we saw in looking at examples of ACE relations, identifying
relations accurately may depend heavily on first identifying
and classifying their arguments accurately. The relation is
quite different for French auto worker and Ford auto
worker. In particular, many relation taggers operate by
considering every pair of entity mentions (basically, noun phrases
representing people, places, or organizations) and applying a
classifier to the pair, returning either no relation or
the type of some relation. Poor performance of the entity
mention tagger severely compromises the relation tagger.

To make matters worse, what the relation tagger finds is a relation
between noun phrases. (In ACE parlance, they are relation
mentions which represent relations between entity mentions.)
If the noun phrase is an anaphoric reference to a named entity,
["Bernard Madoff was jailed yesterday. ... His wife Mabel remained
in seclusion. or ... The swindler's wife, Mabel, remained in
seclusion.] we really want to recover the name of the entity.
This requires establishing coreference between entity mentions.

Semi- and un-supervised methods

All these methods involve bootstrapping which alternates between
finding pairs of arguments and finding the contexts ('patterns') of
these arguments. This is analogous to the co-training (between
spelling features and context features) used in semi-supervised NE
tagging.

All of these methods are based on named arguments ... anaphoric
arguments are not considered.